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{\bf Abstract of Bachelor Thesis} \\

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{\bf An approach to fast malware classification with machine learning technique}
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  With the rapid increase of malware, it is important for malware analysis to classify unknown malware files into malware families. By doing so, the behavior and characteristics of malware will be identified accurately. In this paper, an approach was introduced to perform fast malware classification based on meta-data of malware's file. A machine learning technique called decision tree algorithm, is used to classify malware rapidly and correctly. Experimental results of the malware samples show that the system successfully determined some semantic malware similarities, especially showed their inner similarities in behavior and static malware characteristic.  \\

Keywords:

Malware, static analysis, decision tree.

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{\bf Pham Van Hung}\\
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{\bf Faculty of Environment and Information Studies}\\
{\bf Keio University}\\
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